DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this site, please visit your Hub's overview via https://hub.arcgis.com

Digital orthoimagery for the Denver metropolitan region. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This data is updated quarterly using the latest information from the Fiscal Office and is meant to show properties utilizing the popular economic development tools of tax incentives program for abatements and TIFs. Abatements allow for property tax to be waived for any increase in property value resulting from a qualifying property improvement. TIFs allow for the redirecting of property tax resulting any increase in property value resulting from a qualifying improvement to assist in financing the property improvement or infrastructure needed for the property improvement. Abatements and TIF programs are approved by the State of Ohio and administered by the County Fiscal Office.More information regarding TIFs and Abatements can be found in the Fiscal GIS Hub:https://fiscalgishub.cuyahogacounty.us/pages/incentive-information-site

The U.S. Geological Survey (USGS), in cooperation with the Puerto Rico Environmental Quality Board, has compiled a series of geospatial datasets for Puerto Rico to be implemented into the USGS StreamStats application (https://streamstats.usgs.gov/ss/). These geospatial datasets, along with basin characteristics datasets for Puerto Rico published as a separate USGS data release (https://doi.org/10.5066/P9HK9SSQ), were used to delineate watersheds and develop the peak-flow and low-flow regression equations used by StreamStats. The geospatial dataset described herein are the digital elevation model rasters from NED, at a 10-m resolution, elevations in centimeters. Data are partitioned into four TIFF files, one for each of the four 8-digit Hydrologic Unit Code (HUC) areas for Puerto Rico: 21010002, 21010003, 21010004, and 21010005.

This submission includes both the original product resolution (OPR) and LiDAR point cloud (LPC) LiDAR data collected as part of GeoDAWN: Geoscience Data Acquisition for Northwestern Elko County, Nevada.

The USGS Earth Mapping Resources Initiative (EarthMRI) and USGS 3D Elevation Program (3DEP), Department of Energy Geothermal Technologies Office, Natural Resources Conservation Services, and Bureau of Land Management have partnered to conduct airborne geophysical and 3DEP lidar surveys over parts of Nevada and California to collect information on undiscovered geothermal, critical mineral, and groundwater resources in the western Great Basin and the Walker Lane region.

This 1m Digital Surface Model (DSM) shaded relief is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM shaded relief has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. This shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM dataset is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This app will allow you to interactively download 2020 orthophoto imagery in TIFF format. The images were taken in June of 2020.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

The interpreted polygons were manually transferred to overlays that were registered to the base maps. Map unit attributes and appropriate physiognomic modifier codes were added to a second overlay. The overlays were subsequently rechecked for accuracy. Each overlay of transferred data was scanned using a large format sheet fed scanner at a resolution of 400 dots per inch. The resulting Tagged Image File Format (TIFF) images were then converted to a grid using ArcInfo (Version 7.2.1 Patch 2, Environmental Systems Research Institute, Redlands, California). For data produced with the DOQ base maps, the converted grid was projected to UTM Zone 15 using North American Datum of 1983 (NAD83).

From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at Boyd Corners Reservoir during September 2017. Depth data were collected primarily with a multibeam echosounder. Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry data with lidar elevation data surrounding the reservoirs; gaps in the combined data were estimated (for example the tops of submerged islands) or interpolated. Files included in this Data Release include: grids (tiff format) of reservoir bed elevation, data source, and cell data standard deviation; shapefiles of elevation contours at a 2-foot interval and of single-beam echosounder quality assurance points; and text files (comma-separated value format) of elevation-area-capacity table, measured GNSS points, water surface elevation time series (tides) used to process echosounder data, measured sound velocity profiles, and average sound velocity profiles used to process echosounder data. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

The Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014; Bock and others, 2020) is a dataset of hydrographic features and spatial data designed for use within the National Hydrologic Model that covers the conterminous United States (CONUS), Hawaii, and most major river basins that flow in from Canada. This U.S. Geological Survey (USGS) data release consists of the geospatial fabric features and other related spatial datasets created to expand the National Hydrologic Model to Alaska. This child item consists of topographic data themes that cover the Geospatial Fabric for National Hydrologic Modeling, Alaska Domain. The 30-meter (m) raster datasets included under Topographic Derivatives are: digital elevation (dem.tif), topographic wetness index (TWI, twiX100.tif), slope (rise over run, slope.tif), aspect (asp.tif), flow accumulation (fac.tif), and flow direction (fdr.tif). All file formats are in GeoTIFF (Geographic Tagged Imaged Format), and are sourced from ...

Data about the top and bottom altitude, depth from land surface and/or the thickness of three geologic units in Tennessee were converted into geospatial format for this USGS data release from previously published paper maps and converted into digital formats for use by the public. The three geologic units were the Chattanooga Shale of Mississippian-Devonian age (Moore and Horton, 1999), the Wells Creek Dolomite of middle Ordovician age (Smith, 1959), and the Knox Group of lower Ordovician age (Newcome, 1954). These geologic units represent important geologic horizons across Tennessee. Geologic structure maps provide important information and, in digital format, support investigative and modeling efforts pertaining to water and mineral resources. Prior to this work, the paper source maps used for this data release existed in limited quantities, mainly restricted to the Nashville, TN offices of the Tennessee Department of Environment and Conservation (TDEC) and United States Geological Survey (USGS). The work for this project included (1) scanning and georeferencing original paper maps to create georeferenced images (GRI), (2) digitizing well location points and contour lines, (3) populating well and contour attribute tables with data from maps and associated reports, and (4) when possible, interpolating raster surfaces for the three geologic units of top and bottom altitude, depth from land surface to the top and bottom surface, and thickness. All raster surfaces were aligned to a modified version of the National Hydrogeologic Grid (Clark and others, 2018) to support USGS Lower Mississippi Gulf Water Science Center efforts to create a statewide hydrogeologic framework. All horizontal coordinated data are projected to NAD 1983 USGS Contiguous USA Albers. The raster vertical coordinate information was referenced to the North American Vertical Datum of 1988 (NAVD 88). This data release includes GRIs, vector data of the wells and mapped contours of top, bottom, or thickness, raster data, and related metadata files for each three geologic units under the associated child item tab. Dataset types can be identified by the following naming convention: i_ = georeferenced map images (GRI) po_ = points c_ = contours and closed depressions f_= faults and other structural features p_ = extent polygon ra_ = altitude raster rd_ = depth from land surface raster rt_ = thickness raster The datasets included on this main landing page are as follows: project_metadata.xml – metadata file for general project information studyarea_ext.zip: p_chttshl_ext.shp - mapped extent of the Chattanooga Shale in Tennessee p_wllscr_ext.shp - mapped extent of the Wells Creek Dolomite in Tennessee p_knx_ext.shp - mapped extent of the Knox Group in Tennessee The datasets included on the child item pages are as follows: Chattanooga Shale: geospatial geologic structural datasets in Tennessee: chttshl_metadata.xml - metadata file chttshl_alldata.zip: GRI/ i_chttshl_btm.tif - structure contour map of the bottom of the Chattanooga Shale (Moore and Horton, 1999) i_chttshl_data.tif - map of data used to create structure and isopach maps (Moore and Horton, 1999) i_chttshl_thk.tif - thickness contour map for the Chattanooga Shale (Moore and Horton, 1999) polygons/ p_knx_ext.shp - study area extent for the Chattanooga Shale p_hohenwald.shp - polygon for extend of the Hohenwald Platform (Moore and Horton, 1999) - supplemental data rasters/ ra_chttshl_btm.tif - altitude raster for the bottom of the Chattanooga Shale ra_chttshl_tp.tif - altitude raster for the top of the Chattanooga Shale rd_chttshl_btm.tif - depth from land surface raster of the bottom of the Chattanooga Shale rd_chttshl_tp.tif - depth from land surface raster of the top of the Chattanooga Shale rt_chttshl.tif - thickness raster for the Chattanooga Shale vectors/ c_chttshl_btm.shp - structure contours for the bottom of the Chattanooga Shale c_chttshl_btm_modified.shp - modified structure contours for the bottom of the Chattanooga Shale (hachures removed from closed basins). This vector used to interpolated raster for the bottom of the Chattanooga Shale c_chttshl_thk.shp - thickness contours for the Chattanooga Shale c_chttshl_thk_modified.shp - modified thickness contours for the Chattanooga Shale (hachures removed from closed basins). This vector used to interpolated raster for the thickness of the Chattanooga Shale po_chttshl.shp - point data of altitude and thickness for the Chattanooga Shale Knox Group: geospatial geologic structural datasets in Middle Tennessee: knx_metadata.xml - metadata file knx_alldata.zip: GRI/ i_knx_tp.tif - structure contour map on the top of the Knox Group (Newcome, 1954) i_knx_outcrop.tif - map of the Wells Creek Disturbance (Wilson and Stearns, 1968) polygons/ p_chttshl_ext.shp - study area extent for the Knox Group p_hohenwald.shp - extent of the Hohenwald Platform - supplemental data rasters/ ra_knx_tp.tif - altitude raster for the top of the Knox Group rd_knx_tp.tif - depth from land surface raster of the top of Knox Group vectors/ c_knx_tp.shp - structure contours for the top of the Knox Group c_knx_tp_modified.shp - modified structure contours for the top of the Knox Group (hachures removed from closed basins). This vector used to interpolated raster for the top of the Knox Group po_knx_tp.shp - point data for the altitude of top of the Knox Group Wells Creek Dolomite: geospatial geologic structural datasets in Tennessee: wllscr_metadata.xml - metadata file wllscr_alldata.zip: GRI/ i_wllscr.tif - thickness contour map for the Wells Creek Dolomite (Smith, 1959) polygons/ p_wllscr_ext.shp - study area extent for the Wells Creek Dolomite rasters/ ra_wllscr_btm.tif - altitude raster for the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) ra_wllscr_tp.tif - altitude raster for the top of the Wells Creek Dolomite rd_wllscr_btm.tif - depth from land surface raster of the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) rd_wllscr_tp.tif - depth from land surface raster of the top of the Wells Creek Dolomite rt_wllscr.tif - thickness raster for the Wells Creek Dolomite vectors/ c_wllscr.shp - thickness contours for the Wells Creek Dolomite po_wllscr.shp - point data for the thickness of Wells Creek Dolomite References: Moore, J.L., and Horton, A.B., 1999, Structure and Isopach Maps of the Chattanooga Shale in Tennessee, Tennessee Dept. of Conservation, Division of Geology, Report of Investigations 48, 3 plates. Newcome, R. Jr., 1954, Structure contour map on top of the Knox Dolomite in Middle Tennessee, Tennessee Division of Geology, Ground-Water Investigations Preliminary Chart 5, 1 sheet. Smith, O. Jr., 1959, Isopach map of the Wells Creek Dolomite in Middle Tennessee: Tennessee Division of Water Resources, one sheet. Wilson, C.W. and Stearns, R.G., 1968 Geology of the Wells Creek Structure, Tennessee: Tennessee Division of Geology, Bulletin 68, 248 p.

From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at West Branch Reservoir during September 2017, October 2017, and October 2019. Depth data were collected primarily with a multibeam echosounder; additional bathymetry points were measured using an acoustic Doppler current profiler (ADCP). Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry data with lidar elevation data surrounding the reservoirs; gaps in the combined data were estimated (for example the tops of submerged islands) or interpolated. Files included in this Data Release include: grids (tiff format) of reservoir bed elevation, data source, and cell data standard deviation; shapefiles of elevation contours at a 2-foot interval and of single-beam echosounder quality assurance points; and text files (comma-separated value format) of elevation-area-capacity table, measured GNSS points, water surface elevation time series (tides) used to process echosounder data, measured sound velocity profiles, and average sound velocity profiles used to process echosounder data. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Meeting increasing future electricity demand in the United States will require extensive and explorative planning due to advancing climatic, socioeconomic, and decarbonization policy drivers. Accounting for the response of changes in these drivers on the energy system are made even more complex when considering them in aggregate form with regionally relevant land and technology constraints that narrow where power plants capable of supporting increasing demand will be feasible to operate under uncertain futures. We offer the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package as a high-resolution product to readily evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States for alternative energy futures. GRIDCERF provides 269 suitability layers for use with 56 power plant technology configurations in a harmonized format readily ingestible by geospatially-enabled modeling software. GRIDCERF comes equipped with pre-compiled technology-specific suitability layers but also allows for user customization to robustly address science objectives when evaluating varying future conditions.

Contents:

Common Rasters:

Suitability Layer	GRIDCERF Raster Name
Agricultural Research Service Lands33	gridcerf_ars_lands_2020_conus.tif
Bureau of Indian Affairs (BIA) Land Area Representation Dataset34	cerf_bia_tribal_lands_2019.tif
Bureau of Land Management (BLM) National Landscape Conservation System (NLCS) - National Monuments35	gridcerf_blm_nlcs_national_monument_2021_conus.tif
BLM NLCS - Outstanding Natural Areas36	gridcerf_blm_nlcs_outstanding_natural_areas_2017_conus.tif
BLM NLCS - Trails Historic West37	gridcerf_blm_nlcs_trails_historic_west_buff_1km_2019_conus.tif
BLM NLCS System - Trails Scenic East37	gridcerf_blm_nlcs_trails_scenic_east_buff_1km_2019_conus.tif
BLM NLCS System – Wilderness38	gridcerf_blm_nlcs_wilderness_2021_conus.tif
BLM NLCS - Wilderness Study Areas38	gridcerf_blm_nlcs_wilderness_study_areas_2021_conus.tif
BLM NLCS - Scenic Rivers39	gridcerf_blm_scenic_rivers_1km_2009_conus.tif
National Park Service (NPS) Class 1 airsheds40	gridcerf_class1_airsheds_2015_conus.tif
BLM NLCS National Conservation Areas35	gridcerf_cons_monu_desig_2021_conus.tif
U.S. Fish and Wildlife Service (USFWS) - Critical Habitat41	gridcerf_fws_critical_habitat_2019_conus.tif
USFWS - Land Interests42	gridcerf_fws_land_interests_2019_conus.tif
USFWS - Lands43	gridcerf_fws_lands_2021_conus.tif
USFWS - National Wildlife Refuges42	gridcerf_fws_national_wildlife_refuges_2019_conus.tif
USFWS - Special Designation42	gridcerf_fws_special_designation_2019_conus.tif
National Land Cover Dataset (NLCD) Wetlands44	gridcerf_nlcd_wetlands_1km_2019_conus.tif
NPS Administrative Boundaries45	gridcerf_nps_administrative_boundaries_2020_conus.tif
NPS Lands46	gridcerf_nps_lands_2019_conus.tif
BLM NLCS - Wild & Scenic Rivers39	gridcerf_nwrs_buff_1km_2021_conus.tif
U.S. Forest Service (USFS) Administrative Boundaries47	gridcerf_usfs_administrative_boundaries_2021_conus.tif
USFS lands43	gridcerf_usfs_lands_2021_conus.tif
U.S. Geological Survey (USGS) National Wilderness Lands48	gridcerf_wilderness_lands_2021_conus.tif
USGS Protected Areas of the U.S - Class 1&249	gridcerf_usgs_padus_class_1_to_2_2018_conus.tif
U.S. State Protected Lands50	gridcerf_wdpa_state_protected_lands_2021_conus.tif
Nature Conservancy lands51	gridcerf_wdpa_tnc_managed_lands_2016_conus.tif
USFS Wilderness Areas52	gridcerf_usfs_wilderness_ares_2015_conus.tif

Technology-specific Rasters:

Suitability Layer	GRIDCERF Raster Name
Slope 10% or less suitable22	gridcerf_srtm_slope_5pct_or_less.tif
Slope 10% or less suitable22	gridcerf_srtm_slope_10pct_or_less.tif
Slope 12% or less suitable22	gridcerf_srtm_slope_12pct_or_less.tif
Slope 20% or less suitable22	gridcerf_srtm_slope_20pct_or_less.tif
Airports (10-mile buffer)53	gridcerf_airports_10mi_buffer_conus.tif
Airports (3-mile buffer)53	gridcerf_airports_3mi_buffer_conus.tif
Proximity to Railroad and Navigable Waters (< 5 km)54,55	gridcerf_railnodes5km_navwaters5km_conus.tif
Coal Supply54–56	gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif
NTAD CO Non-attainment Areas57	gridcerf_naa_co_1km_2013_conus.tif
NTAD NOx Non-attainment Areas57	gridcerf_naa_nox_1km_2013_conus.tif
NTAD Ozone Non-attainment Areas57	gridcerf_naa_ozone_1km_2018_conus.tif
NTAD Lead Non-attainment Areas57	gridcerf_naa_pb_1km_2017_conus.tif
NTAD PM10 Non-attainment Areas57	gridcerf_naa_pm10_1km_2013_conus.tif
NTAD PM2.5 Non-attainment Areas57	gridcerf_naa_pm25_1km_2016_conus.tif
NTAD SOx Non-attainment Areas57	gridcerf_naa_sox_1km_2021_conus.tif
Earthquake Potential58	gridcerf_earthquake_pga_0.3g_at_2pct_in_50yrs_2016_conus.tif
Densely population areas12	gridcerf_densely_populated_ssp[2,3,5]_[year].tif
Densely population areas buffered by 25 miles12	gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif
Densely population areas – nuclear12	gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif
National Hydrography Dataset (version 2;

Metadata Link Land Cover 2019

        Abstract: 
        A gridded land cover map derived from aerial imagery and lidar terrain data.





 General Information 


      Source Year
      2019


      Category
      Elevation Model


      Feature Type
      Grid LC


      Methodology
      High resolution land cover dataset for Kitchener,ON. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2019 LiDAR data and 2019 3 band orthomosaic imagery. Ancillary data sources included GIS data provided by Kitchener,ON or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 Meters and all observable errors were corrected.


      Geographic Extent
      City of Kitchener


      Spatial Projection
      NAD83 UTM Zone 17N (EPSG: 26917)


 Georeferencing and Accuracy 


      Acquistion Period
      20190819


      Horizontal Datum
      North American Datum 1983 (EPSG: 6269)


      Vertical Datum
      Canadian Geodet


      Horizontal Accuracy
      NA


      Vertical Accuracy
      NA


      Grid Resolution
      1 m


      Spectral Bands



      Point Density
      NA


      File Format
      LAS, IMG


 Source and Contraints   


      Use Constraint
      Open Data 


      Agency Originator
      University of Vermont


      Agency Distributor
      City of Kitchener


      Process Description
      Aerial Surveying Lidar Data


      Flying Height Above Ground (m)
      1100


      Spatial Area (km2)
      139


      Spatial Resolution (cm)



      Producer
      Airborne Imaging


      System
      Leica ALS7


      Owner
      City of Kitchener


 Contact and Links   


      Open Data
      https://app2.kitchener.ca/appdocs/opendata/ORTHO/Land_Cover_2019_Kitchener.tif


      Contact
      Manager Geospatial Data and Analytics, City of Kitchener


 Citation 


      Citation
      Land Cover 2019 (20190819 - 15 cm) Point Density NA, Horizontal Accuracy: NA, Vertical AccurcacyNAm, (Airborne Imaging for City of Kitchener) NAD83 UTM Zone 17N (EPSG: 26917)



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This 1m Digital Surface Model (DSM) is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

The Wells Creek Dolomite is the lowest unit of the Stones River Group. The Wells Creek consists of cherty limestone that underlies the Murfreesboro Limestone of the Stones River Group of Middle Ordovician age, and directly overlies the Knox Group of early Ordovician and late Cambrian age. The unit ranges in thickness from less than 1.52 meters (5 feet) in the eastern part of the Central Basin to approximately 54.86 meters (180 feet) in Stewart County. The depth of the Wells Creek generally ranges from 121.92 meters to 457.2 meters (from 400 to 1500 feet) below land surface (Smith, 1959). The Wells Creek Dolomite does not yield water but it can be easily recognized when drilling wells and it overlies the Knox Group, a deep aquifer in middle Tennessee and a significant source of groundwater for some areas. For this data release, the raster interpolated for the top of the Knox Group in Middle Tennessee (ra_knx_tp.tif) was also used to represent the altitude of the bottom of the Wells Creek Dolomite (See process steps for more details). An isopach map of the thickness of the Wells Creek Dolomite in Middle Tennessee was prepared as a part of a cooperative groundwater study by the Tennessee Division of Geology and the U.S. Geological Survey (USGS) (Smith, 1959). The 43 x 71 cm map covers parts of Middle Tennessee, from Camden east to Crossville, and from the Kentucky border to the Alabama border. The map scale is approximately 1:600,000. Contour intervals are 5 feet (east of the 100-foot isopach) and 10 feet (west of the 100-foot isopach), which show the thickness of the Wells Creek Dolomite. The associated data are altitude values from a study of cuttings in 120 wells between the western boundary of the Cumberland Plateau and the Tennessee River (Smith, 1959). Prior to the current work, the Wells Creek Dolomite isopach map (Smith, 1959) existed in limited quantities, mainly restricted to the Nashville, TN offices of the Tennessee Department of Environment and Conservation (TDEC) and United States Geological Survey (USGS). The work for this project consisted of (1) scanning and georeferencing original paper maps to create georeferenced images (GRI), (2) digitizing well _location points and contour lines, (3) populating well and contour attribute tables with data from maps and associated reports, and (4) interpolating raster surfaces for the thickness of the Wells Creek Dolomite using the data from the isopach map (Smith, 1959), altitude of the bottom of the Wells Creek Dolomite by using the data for the top of the Knox Group (Newcome, 1954), altitude of the top of the Wells Creek Dolomite using the bottom of the Wells Creek Dolomite added to the thickness of the Wells Creek Dolomite (Smith, 1959), and depth from land surface to the top and bottom of the Wells Creek (USGS, 2012). All raster surfaces were aligned to a modified version of the National Hydrogeologic Grid (Clark and others, 2018) to support USGS Lower Mississippi Gulf Water Science Center efforts to create a statewide hydrogeologic framework. All horizontal coordinated data are projected to NAD 1983 USGS Contiguous USA Albers. Raster vertical coordinate information was referenced to the North American Vertical Datum of 1988 (NAVD 88). Dataset types can be identified by the following naming convention: "i_" = georeferenced map images (GRI) "po_" = points "c_" = contours "p_" = extent polygons "ra_" = altitude raster "rd_" = depth from land surface raster "rt_" = thickness raster The datasets included on this child item page are as follows: wllscr_metadata.xml - metadata file wllscr_alldata.zip: GRI/ i_wllscr.tif - thickness contour map for the Wells Creek Dolomite (Smith, 1959) polygon/ p_wllscr_ext.shp - study area extent for the Wells Creek Dolomite rasters/ ra_wllscr_btm.tif - altitude raster for the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) (NAVD 88) (meters) ra_wllscr_tp.tif - altitude raster for the top of the Wells Creek Dolomite (NAVD 88) (meters) rd_wllscr_btm.tif - depth from land surface raster of the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) (meters) rd_wllscr_tp.tif - depth from land surface raster of the top of the Wells Creek Dolomite (meters) rt_wllscr.tif - thickness raster for the Wells Creek Dolomite (meters) vectors/ c_wllscr.shp - thickness contours for the Wells Creek Dolomite po_wllscr.shp - point data for the thickness of Wells Creek Dolomite References: Clark, B.R., Barlow, P.M., Peterson, S.M., Hughes, J.D., Reeves, H.W., Vigor, R.J., 2018, National-Scale Grid to Support Regional Groundwater Availability Studies and a National Hydrogeologic Framework, U.S. Geological Survey, ScienceBase data release, doi:10.5066/F7P84B24. Newcome, R. Jr., 1954, Structure contour map on top of the Knox Dolomite in Middle Tennessee, Tennessee Division of Geology, Ground-Water Investigations Preliminary Chart 5, 1 sheet. Smith, Ollie, Jr., 1959, Isopach Map of the Wells Creek Dolomite in Middle Tennessee: Tennessee Division of Water Resources, one sheet Wilson, C.W. and Stearns, R.G., 1968 Geology of the Wells Creek Structure, Tennessee: Tennessee Division of Geology, Bulletin 68, 248 p.

These geospatial files are the essential components for the Geologic Map of the Stibnite Mining Area in Valley County, Idaho, which was published by the Idaho Geological Survey in 2022. Three main file types are in this dataset: geographic, geologic, and mining. Geographic files are map extent, lidar base, topographic contours, labels for contours, waterways, and roads. Geologic files are geologic map units, faults, structural lines meaning axial traces, structural points like bedding strike and dip locations, cross section lines, and drill core sample locations. Lastly, mining files are disturbed ground features including open pit polygons or outlines, and general mining features such as the location of an adit. File formats are shape, layer, or raster. Of the 14 shapefiles, 7 have layer files that provide pre-set symbolization for use in ESRI ArcMap that match up with the Geologic Map of the Stibnite Mining Area in Valley County, Idaho. The lidar data have two similar, but distinct, raster format types (ESRI GRID and TIFF) intended to increase end user accessibility. This dataset is a compilation of both legacy data (from Smitherman’s 1985 masters thesis published in 1988, Midas Gold Corporation employees, the Geologic Map of the Stibnite Quadrangle (Stewart and others, 2016) and Reed S. Lewis of the Idaho Geological Survey) and new data from 2013, 2015, and 2016 field work by Niki E. Wintzer.

Geospatial data about City of Dallas, Texas Fort Worth Avenue TIF Parcels. Export to CAD, GIS, PDF, CSV and access via API.

From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at Amawalk Reservoir from May 2018 to November 2019. Depth data were collected primarily with a multibeam echosounder. Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry data with lidar elevation data surrounding the reservoirs; gaps in the combined data were estimated (for example the tops of submerged islands) or interpolated. Files included in this Data Release include: grids (tiff format) of reservoir bed elevation, data source, and cell data standard deviation; shapefiles of elevation contours at a 2-foot interval and of single-beam echosounder quality assurance points; and text files (comma-separated value format) of elevation-area-capacity table, measured GNSS points, water surface elevation time series (tides) used to process echosounder data, measured sound velocity profiles, and average sound velocity profiles used to process echosounder data. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.